Cooperative transportation scheduling : an application domain for DAI

A multiagent approach to designing the transportation domain is presented. The MARS system is described which models cooperative order scheduling within a society of shipping companies. We argue why Distributed Artificial Intelligence (DAI) offers suitable tools to deal with the hard problems in this domain. We present three important instances for DAI techniques that proved useful in the transportation application: cooperation among the agents, task decomposition and task allocation, and decentralised planning. An extension of the contract net protocol for task decomposition and task allocation is presented; we show that it can be used to obtain good initial solutions for complex resource allocation problems. By introducing global information based upon auction protocols, this initial solution can be improved significantly. We demonstrate that the auction mechanism used for schedule optimisation can also be used for implementing dynamic replanning. Experimental results are provided evaluating the performance of different scheduling strategies

Klassische Kommunikations- und Koordinationsmodelle

Bei der Betrachtung von Multi-Agenten-Systemen (MAS) als Teilbereich der Verteilten Künstlichen Intelligenz treten Probleme zutage, die der klassischen Informatik nicht unbekannt sind. Im folgenden werden bekannte klassische Verfahren zur Kommunikation und Koordination in Verteilten Systemen wie sie in Verteilten Betriebssystemen, in Verteilten Programmiersprachen und in Verteilten Datenbanksystemen zur Anwendung kommen, vorgestellt. Die Verwendbarkeit dieser Methoden im Rahmen der Aufgabenstellung des Projektes AKA-MOD wird allgemein untersucht und am Beispiel eines dort verwendeten Szenarios von Transportunternehmen, des sog. Speditions-Szenarios, veranschaulicht

A DAI approach to modeling the transportation domain

A central problem in the study of autonomous cooperating systems is that of how to establish mechanisms for controlling the interactions between different parts (which are called agents) of the system. One way to integrate such mechanisms into a multi-agent system is to exploit the technique of cooperation or negotiation protocols. In a protocol we distinguish to essential layers: the communication layer specifying the possible flow of messages between different agents, and the decision layer, which controls the selection of a message (speech-act) that the agent sends in a specific situation. In this report we first give a short introduction of our agent model InteRRap which provides the basis for the modeling of the different scenarios considered in the AKA-Mod project at the DFKI. The techniques we will discuss in the following are located in the plan based component and in the cooperation component of this model. The domain of application is the MARS scenario (Modeling a Multi-Agent Scenario for Shipping Companies) which implements a group of shipping companies whose goal it is to deliver a set of dynamically given orders, satisfying a set of given time and/or cost constraints. The complexity of the orders may exceed the capacities of a single company. Therefore, cooperation between companies is required in order to achieve the goal in a satisfactory way. This domain is of considerable interest for studies with economical background as well as for research projects. We give a short summary of results from economical studies that are concerned with the real-world situation in Germany in the transportation domain. They show the need for the development of new techniques from the field of computer science to tackle the problems therein. Then, an overview on related research is presented. Two approaches are discussed in more detail: the first one being based on OR-techniques and a second one being based on the concept of partial intelligent agents attempting to integrate techniques from OR and DAI. Both approaches are concerned with the situation in a single company. However, our purpose to handle the case of distributed shipping companies requires additional mechanisms, e.g. to cope with the problems of task allocation and task decomposition in multi-agent systems. Mechanisms for distributed task decomposition and task allocation processes in multi-agent systems belong to the core of our studies. Therefore, we will first discuss techniques for these problems in a general setting and then describe their implementations in the MARS system. In this description, particular emphasis is placed on the cooperation within a shipping company. Here, one company agent has to allocate a set of orders its truck agents. The truck agents support the company agents by giving cost estimations based on their route planning facility. Thus, this procedure provides the basis for the decisions of the company agents and is discussed in very detail. Finally, we present results from a series of benchmark tests. The test sets have also been run with OR-implementations and thus, give us the opportunity to compare our implementation against these approaches

The agent architecture InteRRaP : concept and application

One of the basic questions of research in Distributed Artificial Intelligence (DAI) is how agents have to be structured and organized, and what functionalities they need in order to be able to act and to interact in a dynamic environment. To cope with this question is the purpose of models and architectures for autonomous and intelligent agents. In the first part of this report, InteRRaP, an agent architecture for multi-agent systems is presented. The basic idea is to combine the use of patterns of behaviour with planning facilities in order to be able to exploit the advantages both of the reactive, behaviour-based and of the deliberate, plan-based paradigm. Patterns of behaviour allow an agent to react flexibly to changes in its environment. What is considered necessary for the performance of more sophisticated tasks is the ability of devising plans deliberately. A further important feature of the model is that it explicitly represents knowledge and strategies for cooperation. This makes it suitable for describing high-level interaction among autonomous agents. In the second part of the report, the loading-dock domain is presented, which has been the first application the InteRRaP agent model has been tested with. An automated loading-dock is described where the agent society consists of forklifts which have to load and unload trucks in a shared, dynamic environment

Kooperierende Agenten

Das vorliegende Dokument gibt eine Übersicht über die Aktivitäten des DFKI im Arbeitsbereich \u27Kooperierende Systeme\u27. Es werden die laufenden Projekte des Bereichs allgemein vorgestellt. Die Systeme (Entwicklungsumgebungen) der einzelnen Projekte werden eingeführt und Anwendungen unter Benutzung verschiedener Kooperationstechniken beschrieben. Ferner werden Techniken auf theoretischer Basis abgehandelt.The document in hand displays an overview of the DFKI activities in the field of \u27Cooperating Systems\u27. Current projects within the field will be presented. The systems and tools of the different projects will be introduced and the applications will be described with a special view on the different cooperation techniques. Eventually some specific techniques will be discussed on a more theoretical level

Kooperierende Agenten

Das vorliegende Dokument gibt eine Übersicht über die Aktivitäten des DFKI im Arbeitsbereich 'Kooperierende Systeme'. Es werden die laufenden Projekte des Bereichs allgemein vorgestellt. Die Systeme (Entwicklungsumgebungen) der einzelnen Projekte werden eingeführt und Anwendungen unter Benutzung verschiedener Kooperationstechniken beschrieben. Ferner werden Techniken auf theoretischer Basis abgehandelt.The document in hand displays an overview of the DFKI activities in the field of 'Cooperating Systems'. Current projects within the field will be presented. The systems and tools of the different projects will be introduced and the applications will be described with a special view on the different cooperation techniques. Eventually some specific techniques will be discussed on a more theoretical level

Task Decomposition in Dynamic Agent Societies*

Abstract The Contract Net Protocol is one of the most prominent concepts for negotiation in Multi-Agent Systems. However, there are some unspecified features like the task decomposition procedures used by managers. Also the bottleneck induced by the central role of the managers will be identified as a weak point of the method. This is especially true if the amount of tasks in the system and the society of agents itself are dynamic. We will first investigate in a general task decomposition strategy. Then the central role of the manager will be softened by introducing the Decentralized Task Decomposition Model. Finally, in the Completely Decentralized Model, the manager will be totally eliminated. Our ideas are presented in a transportation domain, which is also described to some extent